Analytics are employed in a wide range of different applications and can be valuable for the breadth of information they provide for almost any given control system. Among other things, analytics can be used to identify meaningful relationships between actual data and target data, which can further be used to evaluate and correct deficiencies within control systems. In climate-control systems, for example, actual temperatures or other relevant parameters within the controlled environment can be monitored relative to target temperatures or parameters in order to assess the functionality of the associated temperature controllers. Although applicable to almost any climate-controlled environment, temperature analytics may be particularly useful in monitoring cabin temperatures within an aircraft which can fluctuate widely over relatively short periods of time. Temperature analytics may also be useful in other mobile environments, such as in passenger vehicles, buses, trains, boats, and the like, as well as in stationary environments, such as in residential buildings, commercial buildings, and the like.
While conventional analytics techniques may be helpful, there is still room for improvement. When used to analyze cabin temperatures in aircraft, for instance, conventional techniques typically provide a mere snapshot comparison of the actual and target temperatures, which fails to adequately characterize functionality. For example, a large difference between actual and target temperatures in one instance could be flagged as a failure even when the actual temperature is properly approaching the target temperature. Other conventional techniques may evaluate the instantaneous change in the cabin temperature in order to determine whether the actual temperature is approaching or deviating from the target temperature. However, momentary deviations between actual and target temperatures are not exclusive to a malfunctioning system, and can often occur even under normal operating conditions. Conventional analytics are thus inadequate for evaluating actual and target temperature parameters, or other fluctuating non-monotonic dependent parameters.
Accordingly, there is a need for improved and more intuitive techniques for monitoring and analyzing non-monotonic parameters which address the foregoing limitations and provide more reliable health evaluations of a controlled system.